{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ggplot: (285176701)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ADKHZBgAAAAyh2QYAAAAModkGAAAADKHZBgAAAAyh2QYAAAAModkGAAAADKHZBgAAAAyh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\ngCF/AxZaHhm94qkkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10593b3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(mpg, aes(x='cty', y='hwy')) + \\\n",
    "    geom_point() + \\\n",
    "    ggtitle(\"City vs. Highway Miles per Gallon\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
